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A Wedge Fastening Strength Recognition System for Safety Diagnosis of Large-Capacity Generators and Propulsion Systems For Navy Vessels
2024 IEEE International Conference on Big Data and Smart Computing (BigComp)
Year: 2024 | Conference Paper |
This study was designed to safety management methods for military gas turbine generators. The weakening of the generator wedge's fastening strength due to long-term continuous operation is diagnosed through the acceleration signal generated when hitting, and notifies an user whether the generator wedge needs to be replaced. A uniform blow is made with a mechanical device, and the acceleration sign...Show More
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow
Ki-Ung Song;Dongseok Shim;Kang-Wook Kim;Jae-Young Lee;Younggeun Kim
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Super-resolution suffers from an innate ill-posed, problem that a single low-resolution (LR) image can be from multiple high-resolution (HR) images. Recent studies on the flow-based algorithm solve this ill-posedness by learning the super-resolution space and predicting diverse HR outputs. Unfortunately, the diversity of the super-resolution outputs is still unsatisfactory, and the outputs from th...Show More
NTIRE 2022 Challenge on Learning the Super-Resolution Space
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Kang-Wook Kim;Younggeun Kim;Jae-Young Lee;Zechao Li;Jinshan Pan;Dongseok Shim;Ki-Ung Song;Jinhui Tang;Cong Wang;Zhihao Zhao
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (20)
This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the same low-resolution image, we asked the participants to create methods that sample diverse super-resolution from the space of possible high-resolution images given a low-resolution ima...Show More
Noise Conditional Flow Model for Learning the Super-Resolution Space
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (17)
Fundamentally, super-resolution is ill-posed problem because a low-resolution image can be obtained from many high-resolution images. Recent studies for super-resolution cannot create diverse super-resolution images. Although SRFlow tried to account for ill-posed nature of the super-resolution by predicting multiple high-resolution images given a low-resolution image, there is room to improve the ...Show More
NTIRE 2021 Learning the Super-Resolution Space Challenge
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Christoph Busch;Yang Chen;Jian Cheng;Vishal Chudasama;Ruipeng Gang;Shangqi Gao;Kun Gao;Laiyun Gong;Qingrui Han;Chao Huang;Zhi Jin;Younghyun Jo;Seon Joo Kim;Younggeun Kim;Seungjun Lee;Yuchen Lei;Chu-Tak Li;Chenghua Li;Ke Li;Zhi-Song Liu;Youming Liu;Nan Nan;Seung-Ho Park;Heena Patel;Shichong Peng;Kalpesh Prajapati;Haoran Qi;Kiran Raja;Raghavendra Ramachandra;Wan-Chi Siu;Donghee Son;Ruixia Song;Kishor Upla;Li-Wen Wang;Yatian Wang;Junwei Wang;Qianyu Wu;Xinhua Xu;Sejong Yang;Zhen Yuan;Liting Zhang;Huanrong Zhang;Junkai Zhang;Yifan Zhang;Zhenzhou Zhang;Hangqi Zhou;Aichun Zhu;Xiahai Zhuang;Jiaxin Zou
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (24)
This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a ...Show More
NTIRE 2021 Learning the Super-Resolution Space Challenge
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Christoph Busch;Yang Chen;Jian Cheng;Vishal Chudasama;Ruipeng Gang;Shangqi Gao;Kun Gao;Laiyun Gong;Qingrui Han;Chao Huang;Zhi Jin;Younghyun Jo;Seon Joo Kim;Younggeun Kim;Seungjun Lee;Yuchen Lei;Chu-Tak Li;Chenghua Li;Ke Li;Zhi-Song Liu;Youming Liu;Nan Nan;Seung-Ho Park;Heena Patel;Shichong Peng;Kalpesh Prajapati;Haoran Qi;Kiran Raja;Raghavendra Ramachandra;Wan-Chi Siu;Donghee Son;Ruixia Song;Kishor Upla;Li-Wen Wang;Yatian Wang;Junwei Wang;Qianyu Wu;Xinhua Xu;Sejong Yang;Zhen Yuan;Liting Zhang;Huanrong Zhang;Junkai Zhang;Yifan Zhang;Zhenzhou Zhang;Hangqi Zhou;Aichun Zhu;Xiahai Zhuang;Jiaxin Zou
Year: 2021 | Conference Paper |
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